Network traffic simulation

About: Network traffic simulation is a research topic. Over the lifetime, 4535 publications have been published within this topic receiving 74606 citations.

Intelligent Traffic Control Decision Support System

Abstract: When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control.

Decomposition of Traffic in Loss Systems with Renewal Input

Abstract: A method is proposed for the analysis of a teletraffic network with alternative routing. The approach used is to model a "general link" as a separate entity, being a first step towards analyzing the network in a link-by-link fashion. To this end the general link is considered to be a group of fully available trunks offered a number of separate streams of traffic. By considering the multivariate point process formed by the total stream of arriving calls, the individual streams of traffic overflowing from, and the individual streams of traffic carried on the common trunk, are distinguished. All streams of traffic in the network are considered to be well described by simple renewal processes. The validity of the method is illustrated with exact numerical results and by simulation.

Impact of Information on Network Performance - An Information-Theoretic Perspective

Abstract: Available network information is an important factor in determining network performance. In this paper, we study the basic limits on the amount of network information that should be transmitted in the network to achieve a given level of network performance. From the perspective of information theory, network information is an information source, and the lower bound on network information is the minimum code letters required to encode the source. We propose a general information-theoretic framework, which can be applied to any network, to study the effect of network information on the performance of any network protocol. We also analyze the tradeoff between network performance improvement and network information collection overhead. To illustrate our approach, we use the framework to determine the lower bound on the traffic information for a simple scheduling protocol in wireless networks. The results in this paper may be used to analyze and evaluate network protocols and guide future designs.

A neural network approach for freeway traffic flow prediction

Abstract: Traffic flow modeling is an essential component of any traffic control or monitoring system. This paper presents a new short term traffic flow prediction model based on a feedforward neural network. The structure determination of this neural network is viewed as a system identification problem, and the model performances are validated using both simulation and real traffic data obtained from the I-880 freeway in Hayward, California.